{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (2.1.1)\n",
      "Requirement already satisfied: numpy>=1.23.2 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from pandas) (1.26.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from pandas) (2023.3.post1)\n",
      "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from pandas) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: matplotlib in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (3.8.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (1.1.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (0.12.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (4.43.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (1.4.5)\n",
      "Requirement already satisfied: numpy<2,>=1.21 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (1.26.0)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (23.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (10.0.1)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (3.1.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\zengzhi\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install pandas\n",
    "%pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.layers import *\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.io import loadmat\n",
    "from scipy.io import savemat\n",
    "import math\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "from matplotlib.ticker import LinearLocator\n",
    "\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import seed\n",
    "seed(0)\n",
    "tf.random.set_seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 0. Define the Grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_domain = np.linspace(0, 3*np.pi, 1000)\n",
    "delta = x_domain[1] - x_domain[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Define Physical Info."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    suppose: x = X[0]; u_x = X[1]; dudx = X[2]; du2dx = X[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## one-dimensional nonlinear differential equation containing trigonometric functions and a square operator\n",
    "sin(u^2)+u_x=f(x);\n",
    "f(x)=np.sin((np.sin(x_domain^2))^2) + 2*x_domain*np.cos(x_domain^2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "governing_equation_components = []\n",
    "governing_equation_components.append(Lambda(lambda x: tf.sin(x[1]**2)))\n",
    "governing_equation_components.append(Lambda(lambda x: x[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "governing_equation_mask = x_domain*0 + 1\n",
    "governing_equation_mask[0] = 0\n",
    "governing_equation_mask[-1] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "fx = governing_equation_mask*(np.sin((np.sin(x_domain**2))**2) + 2*x_domain*np.cos(x_domain**2) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimate_equation_form = False\n",
    "equation_component_combination = [1.0,1.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Define the Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    suppose: x = X[0]; u_x = X[1]; dudx = X[2]; du2dx = X[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_components = []\n",
    "observation_components.append(Lambda(lambda x: x[1]))\n",
    "observation_components.append(Lambda(lambda x: x[2]))\n",
    "observation_components.append(Lambda(lambda x: x[3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    format: [x,combination,value]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "observation_data = []\n",
    "observation_data.append([0,[1,0,0],0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Define PICN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[-1.0/(delta)]],[[1.0/(delta)]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient_2_kernal_init(shape, dtype=tf.float32):\n",
    "    return tf.constant([[[1.0/(delta**2)]],[[-2.0/(delta**2)]],[[1.0/(delta**2)]]], dtype=dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = [keras.layers.Input(shape=(1,1)),keras.layers.Input(shape=(len(x_domain),1))]\n",
    "\n",
    "hidden_field = keras.layers.Conv1DTranspose(filters=1, \n",
    "                                            kernel_size=len(x_domain)+4, \n",
    "                                            activation='linear')(inputs[0])\n",
    "coordinates = inputs[1]\n",
    "field = keras.layers.Conv1D(filters=1, \n",
    "                            kernel_size=3, \n",
    "                            padding='valid', \n",
    "                            activation=Lambda(lambda x: x+tf.tanh(x)))(hidden_field)\n",
    "gradient_field = keras.layers.Conv1D(filters=1, \n",
    "                                     kernel_size=2, \n",
    "                                     padding='valid',\n",
    "                                     use_bias=False,\n",
    "                                     trainable=False,\n",
    "                                     kernel_initializer=gradient_kernal_init)(field)\n",
    "gradient_2_field = keras.layers.Conv1D(filters=1, \n",
    "                                       kernel_size=3, \n",
    "                                       padding='valid',\n",
    "                                       use_bias=False,\n",
    "                                       trainable=False,\n",
    "                                       kernel_initializer=gradient_2_kernal_init)(field)\n",
    "phycial_fields = [coordinates,field[:,1:-1,:],gradient_field[:,1:,:],gradient_2_field]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "if estimate_equation_form==True:\n",
    "    tf_governing_equation_components = [component(phycial_fields) for component in governing_equation_components]\n",
    "    concat_equation_components = Lambda(lambda x: tf.concat(x,axis=-1))(tf_governing_equation_components)\n",
    "    governing_equation = keras.layers.Conv1D(filters=1, \n",
    "                                             kernel_size=1, \n",
    "                                             padding='valid',\n",
    "                                             use_bias=False)(concat_equation_components)\n",
    "else:\n",
    "    tf_weighted_governing_equation_components = [weight*component(phycial_fields) for [weight,component] in zip(equation_component_combination,governing_equation_components)]\n",
    "    concat_weighted_equation_components = Lambda(lambda x: tf.concat(x,axis=-1))(tf_weighted_governing_equation_components)\n",
    "    governing_equation = Lambda(lambda x: tf.reduce_sum(x,axis=-1,keepdims=True))(concat_weighted_equation_components)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_observation_components = [component(phycial_fields) for component in observation_components]\n",
    "observation_list = []\n",
    "for data in observation_data:\n",
    "    if data[0] <= x_domain[0]:\n",
    "        left_x_position_index = 0\n",
    "        right_x_position_index = 1\n",
    "        left_x_position_weight = 1\n",
    "        right_x_position_weight = 0\n",
    "    elif data[0] >= x_domain[-1]:\n",
    "        left_x_position_index = -2\n",
    "        right_x_position_index = -1\n",
    "        left_x_position_weight = 0\n",
    "        right_x_position_weight = 1\n",
    "    else:\n",
    "        left_x_position_index = int(np.round((data[0] - x_domain[0])/delta))\n",
    "        right_x_position_index = int(np.round(left_x_position_index + 1))\n",
    "        left_x_position_weight = 1-(data[0] - (x_domain[0]+delta*left_x_position_index))/delta\n",
    "        right_x_position_weight = 1-left_x_position_weight\n",
    "        \n",
    "    left_position_observation_components = [component[:,left_x_position_index,:] for component in tf_observation_components]\n",
    "    right_position_observation_components = [component[:,right_x_position_index,:] for component in tf_observation_components]\n",
    "    \n",
    "    concat_weighted_left_position_observation_components = Lambda(lambda x: tf.concat(x,axis=-1))([weight*component for [weight,component] in zip(data[1],left_position_observation_components)])\n",
    "    concat_weighted_right_position_observation_components = Lambda(lambda x: tf.concat(x,axis=-1))([weight*component for [weight,component] in zip(data[1],right_position_observation_components)])\n",
    "    \n",
    "    left_observation = Lambda(lambda x: tf.reduce_sum(x,axis=-1,keepdims=True))(concat_weighted_left_position_observation_components)\n",
    "    right_observation = Lambda(lambda x: tf.reduce_sum(x,axis=-1,keepdims=True))(concat_weighted_right_position_observation_components)\n",
    "    \n",
    "    observation_list.append(left_x_position_weight*left_observation + right_x_position_weight*right_observation)\n",
    "    \n",
    "observations = Lambda(lambda x: tf.concat(x,axis=-1))(observation_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pde_model = keras.Model(inputs=inputs, outputs=phycial_fields)\n",
    "pde_model_train = keras.Model(inputs=inputs, outputs=[governing_equation,observations])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)        [(None, 1, 1)]               0         []                            \n",
      "                                                                                                  \n",
      " conv1d_transpose (Conv1DTr  (None, 1004, 1)              1005      ['input_1[0][0]']             \n",
      " anspose)                                                                                         \n",
      "                                                                                                  \n",
      " conv1d (Conv1D)             (None, 1002, 1)              4         ['conv1d_transpose[0][0]']    \n",
      "                                                                                                  \n",
      " conv1d_1 (Conv1D)           (None, 1001, 1)              2         ['conv1d[0][0]']              \n",
      "                                                                                                  \n",
      " input_2 (InputLayer)        [(None, 1000, 1)]            0         []                            \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem (  (None, 1000, 1)              0         ['conv1d[0][0]']              \n",
      " SlicingOpLambda)                                                                                 \n",
      "                                                                                                  \n",
      " tf.__operators__.getitem_1  (None, 1000, 1)              0         ['conv1d_1[0][0]']            \n",
      "  (SlicingOpLambda)                                                                               \n",
      "                                                                                                  \n",
      " conv1d_2 (Conv1D)           (None, 1000, 1)              3         ['conv1d[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 1014 (3.96 KB)\n",
      "Trainable params: 1009 (3.94 KB)\n",
      "Non-trainable params: 5 (20.00 Byte)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def model_print_functoin(s):\n",
    "    with open('model_summary.txt','a') as f:\n",
    "        print(s, file=f)\n",
    "\n",
    "pde_model.summary(print_fn=model_print_functoin)\n",
    "pde_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<KerasTensor: shape=(None, 1, 1) dtype=float32 (created by layer 'input_1')>,\n",
       " <KerasTensor: shape=(None, 1000, 1) dtype=float32 (created by layer 'input_2')>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pde_model_train.input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<KerasTensor: shape=(None, 1000, 1) dtype=float32 (created by layer 'lambda_7')>,\n",
       " <KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'lambda_12')>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pde_model_train.output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Prepare the Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "unit_constant = np.asarray([[[1.0]]],dtype=np.float32)\n",
    "training_input_data_0 = np.repeat(unit_constant, 1, axis=0)\n",
    "print(training_input_data_0.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1000, 1)\n"
     ]
    }
   ],
   "source": [
    "training_input_data_1 = np.expand_dims(x_domain.astype(np.float32),axis=[0,-1])\n",
    "print(training_input_data_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1000, 1)\n"
     ]
    }
   ],
   "source": [
    "training_label_data_0 = np.expand_dims(fx,axis=(0,-1))\n",
    "print(training_label_data_0.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 1)\n"
     ]
    }
   ],
   "source": [
    "training_label_data_1 = np.expand_dims(np.asarray([data[2] for data in observation_data]),axis=[0])\n",
    "print(training_label_data_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_input_data = [training_input_data_0,training_input_data_1]\n",
    "training_label_data = [training_label_data_0,training_label_data_1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['loss', 'lambda_7_loss', 'lambda_12_loss']\n"
     ]
    }
   ],
   "source": [
    "pde_model_train.compile(optimizer=keras.optimizers.Adam(), loss=\"mse\")\n",
    "pde_model_train.save_weights('picn_initial_weights.h5')\n",
    "temp_history = pde_model_train.fit(x=training_input_data, y=training_label_data, epochs=1, verbose=0)\n",
    "history_keys = []\n",
    "for key in temp_history.history.keys():\n",
    "    history_keys.append(key)\n",
    "print(history_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def record_predictions():\n",
    "    [_,ux, dudx, d2udx2] = pde_model.predict(training_input_data)\n",
    "    ux_list.append(ux[0,:,0])\n",
    "    dudx_list.append(dudx[0,:,0])\n",
    "    d2udx2_list.append(d2udx2[0,:,0])\n",
    "\n",
    "class Per_X_Epoch_Record(tf.keras.callbacks.Callback):\n",
    "    def __init__(self, record_interval, verbose=1):\n",
    "        super(tf.keras.callbacks.Callback, self).__init__()\n",
    "        self.total_loss = history_keys[0]\n",
    "        self.domain_loss = history_keys[1]\n",
    "        self.bdc_loss = history_keys[2]\n",
    "        self.previous_total_loss = 9999999\n",
    "        self.record_interval = record_interval;\n",
    "        self.verbose = verbose\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        \n",
    "        if epoch%self.record_interval == 0:\n",
    "            \n",
    "            current_total_loss = logs.get(self.total_loss)\n",
    "            current_domain_loss = logs.get(self.domain_loss)\n",
    "            current_bdc_loss = logs.get(self.bdc_loss)\n",
    "            \n",
    "            epoch_number_list.append(epoch)\n",
    "            total_loss_list.append(current_total_loss)\n",
    "            domain_loss_list.append(current_domain_loss)\n",
    "            boundary_loss_list.append(current_bdc_loss)\n",
    "        \n",
    "            if current_total_loss < self.previous_total_loss:\n",
    "                self.previous_total_loss = current_total_loss\n",
    "                pde_model_train.save_weights('picn_best_weights.h5')\n",
    "            \n",
    "            if self.verbose > 0:\n",
    "                print(\"epoch: {:10.5f} | total_loss: {:10.5f} | domain_loss: {:10.5f} | bdc_loss: {:10.5f}\".format(epoch,current_total_loss,current_domain_loss,current_bdc_loss))\n",
    "        \n",
    "            # evaluate the errors in f-domain\n",
    "            record_predictions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = [\n",
    "    Per_X_Epoch_Record(record_interval=200,verbose=1),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:    0.00000 | total_loss:  270.56973 | domain_loss:  300.63208 | bdc_loss:    0.00865\n",
      "1/1 [==============================] - 0s 63ms/step\n",
      "epoch:  200.00000 | total_loss:   52.91827 | domain_loss:   58.79425 | bdc_loss:    0.03446\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch:  400.00000 | total_loss:   49.48975 | domain_loss:   54.97416 | bdc_loss:    0.13015\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch:  600.00000 | total_loss:   38.83280 | domain_loss:   43.14357 | bdc_loss:    0.03589\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch:  800.00000 | total_loss:   19.75036 | domain_loss:   21.94469 | bdc_loss:    0.00140\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 1000.00000 | total_loss:    7.31336 | domain_loss:    8.12593 | bdc_loss:    0.00026\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 1200.00000 | total_loss:    3.14196 | domain_loss:    3.49107 | bdc_loss:    0.00000\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 1400.00000 | total_loss:    1.56430 | domain_loss:    1.73810 | bdc_loss:    0.00007\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 1600.00000 | total_loss:    0.83752 | domain_loss:    0.93054 | bdc_loss:    0.00028\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 1800.00000 | total_loss:    0.48379 | domain_loss:    0.53747 | bdc_loss:    0.00061\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 2000.00000 | total_loss:    0.30152 | domain_loss:    0.33491 | bdc_loss:    0.00106\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 2200.00000 | total_loss:    0.19937 | domain_loss:    0.22133 | bdc_loss:    0.00167\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 2400.00000 | total_loss:    0.13787 | domain_loss:    0.15290 | bdc_loss:    0.00256\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 2600.00000 | total_loss:    0.09890 | domain_loss:    0.10946 | bdc_loss:    0.00385\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 2800.00000 | total_loss:    0.07318 | domain_loss:    0.08069 | bdc_loss:    0.00564\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 3000.00000 | total_loss:    0.05554 | domain_loss:    0.06084 | bdc_loss:    0.00787\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 3200.00000 | total_loss:    0.04293 | domain_loss:    0.04655 | bdc_loss:    0.01039\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 3400.00000 | total_loss:    0.03398 | domain_loss:    0.03632 | bdc_loss:    0.01293\n",
      "1/1 [==============================] - 0s 14ms/step\n",
      "epoch: 3600.00000 | total_loss:    0.02738 | domain_loss:    0.02874 | bdc_loss:    0.01518\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 3800.00000 | total_loss:    0.02240 | domain_loss:    0.02302 | bdc_loss:    0.01679\n",
      "1/1 [==============================] - 0s 12ms/step\n",
      "epoch: 4000.00000 | total_loss:    0.01863 | domain_loss:    0.01874 | bdc_loss:    0.01770\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 4200.00000 | total_loss:    0.01588 | domain_loss:    0.01565 | bdc_loss:    0.01791\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 4400.00000 | total_loss:    0.01358 | domain_loss:    0.01316 | bdc_loss:    0.01733\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 4600.00000 | total_loss:    0.01169 | domain_loss:    0.01119 | bdc_loss:    0.01619\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 4800.00000 | total_loss:    0.01009 | domain_loss:    0.00958 | bdc_loss:    0.01460\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 5000.00000 | total_loss:    0.00901 | domain_loss:    0.00859 | bdc_loss:    0.01281\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 5200.00000 | total_loss:    0.00762 | domain_loss:    0.00725 | bdc_loss:    0.01097\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 5400.00000 | total_loss:    0.00717 | domain_loss:    0.00695 | bdc_loss:    0.00916\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 5600.00000 | total_loss:    0.00648 | domain_loss:    0.00636 | bdc_loss:    0.00760\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 5800.00000 | total_loss:    0.00554 | domain_loss:    0.00546 | bdc_loss:    0.00621\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 6000.00000 | total_loss:    0.00530 | domain_loss:    0.00532 | bdc_loss:    0.00506\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 6200.00000 | total_loss:    0.00439 | domain_loss:    0.00442 | bdc_loss:    0.00412\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 6400.00000 | total_loss:    0.00372 | domain_loss:    0.00377 | bdc_loss:    0.00333\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 6600.00000 | total_loss:    0.00372 | domain_loss:    0.00382 | bdc_loss:    0.00275\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 6800.00000 | total_loss:    0.00367 | domain_loss:    0.00382 | bdc_loss:    0.00229\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 7000.00000 | total_loss:    0.00318 | domain_loss:    0.00332 | bdc_loss:    0.00188\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 7200.00000 | total_loss:    0.00309 | domain_loss:    0.00326 | bdc_loss:    0.00158\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 7400.00000 | total_loss:    0.00273 | domain_loss:    0.00289 | bdc_loss:    0.00132\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 7600.00000 | total_loss:    0.00256 | domain_loss:    0.00272 | bdc_loss:    0.00114\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 7800.00000 | total_loss:    0.00220 | domain_loss:    0.00234 | bdc_loss:    0.00097\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 8000.00000 | total_loss:    0.00275 | domain_loss:    0.00296 | bdc_loss:    0.00085\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 8200.00000 | total_loss:    0.00250 | domain_loss:    0.00269 | bdc_loss:    0.00075\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 8400.00000 | total_loss:    0.00273 | domain_loss:    0.00296 | bdc_loss:    0.00067\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 8600.00000 | total_loss:    0.00206 | domain_loss:    0.00223 | bdc_loss:    0.00059\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 8800.00000 | total_loss:    0.00188 | domain_loss:    0.00203 | bdc_loss:    0.00054\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 9000.00000 | total_loss:    0.00160 | domain_loss:    0.00173 | bdc_loss:    0.00047\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 9200.00000 | total_loss:    0.00285 | domain_loss:    0.00312 | bdc_loss:    0.00043\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 9400.00000 | total_loss:    0.00145 | domain_loss:    0.00156 | bdc_loss:    0.00040\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 9600.00000 | total_loss:    0.00186 | domain_loss:    0.00203 | bdc_loss:    0.00036\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 9800.00000 | total_loss:    0.00151 | domain_loss:    0.00164 | bdc_loss:    0.00032\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 10000.00000 | total_loss:    0.00222 | domain_loss:    0.00243 | bdc_loss:    0.00029\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 10200.00000 | total_loss:    0.00249 | domain_loss:    0.00273 | bdc_loss:    0.00028\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 10400.00000 | total_loss:    0.00138 | domain_loss:    0.00151 | bdc_loss:    0.00027\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 10600.00000 | total_loss:    0.00142 | domain_loss:    0.00156 | bdc_loss:    0.00024\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 10800.00000 | total_loss:    0.00164 | domain_loss:    0.00180 | bdc_loss:    0.00023\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 11000.00000 | total_loss:    0.00107 | domain_loss:    0.00116 | bdc_loss:    0.00021\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 11200.00000 | total_loss:    0.00377 | domain_loss:    0.00416 | bdc_loss:    0.00019\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 11400.00000 | total_loss:    0.00250 | domain_loss:    0.00275 | bdc_loss:    0.00019\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 11600.00000 | total_loss:    0.00157 | domain_loss:    0.00172 | bdc_loss:    0.00017\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 11800.00000 | total_loss:    0.00149 | domain_loss:    0.00163 | bdc_loss:    0.00015\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 12000.00000 | total_loss:    0.00132 | domain_loss:    0.00145 | bdc_loss:    0.00014\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 12200.00000 | total_loss:    0.00195 | domain_loss:    0.00216 | bdc_loss:    0.00014\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 12400.00000 | total_loss:    0.00162 | domain_loss:    0.00178 | bdc_loss:    0.00013\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 12600.00000 | total_loss:    0.00146 | domain_loss:    0.00161 | bdc_loss:    0.00011\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 12800.00000 | total_loss:    0.00231 | domain_loss:    0.00256 | bdc_loss:    0.00011\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 13000.00000 | total_loss:    0.00098 | domain_loss:    0.00108 | bdc_loss:    0.00010\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 13200.00000 | total_loss:    0.00230 | domain_loss:    0.00254 | bdc_loss:    0.00010\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 13400.00000 | total_loss:    0.00168 | domain_loss:    0.00186 | bdc_loss:    0.00010\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 13600.00000 | total_loss:    0.00055 | domain_loss:    0.00061 | bdc_loss:    0.00009\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 13800.00000 | total_loss:    0.00119 | domain_loss:    0.00131 | bdc_loss:    0.00008\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 14000.00000 | total_loss:    0.00154 | domain_loss:    0.00170 | bdc_loss:    0.00008\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 14200.00000 | total_loss:    0.00148 | domain_loss:    0.00164 | bdc_loss:    0.00008\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 14400.00000 | total_loss:    0.00171 | domain_loss:    0.00189 | bdc_loss:    0.00007\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 14600.00000 | total_loss:    0.00082 | domain_loss:    0.00090 | bdc_loss:    0.00007\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 14800.00000 | total_loss:    0.00078 | domain_loss:    0.00086 | bdc_loss:    0.00007\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 15000.00000 | total_loss:    0.00117 | domain_loss:    0.00129 | bdc_loss:    0.00007\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 15200.00000 | total_loss:    0.00144 | domain_loss:    0.00159 | bdc_loss:    0.00006\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 15400.00000 | total_loss:    0.00096 | domain_loss:    0.00107 | bdc_loss:    0.00005\n",
      "1/1 [==============================] - 0s 22ms/step\n",
      "epoch: 15600.00000 | total_loss:    0.00111 | domain_loss:    0.00123 | bdc_loss:    0.00005\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 15800.00000 | total_loss:    0.00220 | domain_loss:    0.00244 | bdc_loss:    0.00005\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "epoch: 16000.00000 | total_loss:    0.00112 | domain_loss:    0.00123 | bdc_loss:    0.00004\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 16200.00000 | total_loss:    0.00111 | domain_loss:    0.00122 | bdc_loss:    0.00004\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "epoch: 16400.00000 | total_loss:    0.00063 | domain_loss:    0.00070 | bdc_loss:    0.00004\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 16600.00000 | total_loss:    0.00086 | domain_loss:    0.00096 | bdc_loss:    0.00004\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 16800.00000 | total_loss:    0.00173 | domain_loss:    0.00191 | bdc_loss:    0.00004\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 17000.00000 | total_loss:    0.00194 | domain_loss:    0.00215 | bdc_loss:    0.00004\n",
      "1/1 [==============================] - 0s 8ms/step\n",
      "epoch: 17200.00000 | total_loss:    0.00094 | domain_loss:    0.00104 | bdc_loss:    0.00003\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 17400.00000 | total_loss:    0.00079 | domain_loss:    0.00088 | bdc_loss:    0.00003\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 17600.00000 | total_loss:    0.00225 | domain_loss:    0.00249 | bdc_loss:    0.00004\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 17800.00000 | total_loss:    0.00097 | domain_loss:    0.00108 | bdc_loss:    0.00003\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 18000.00000 | total_loss:    0.00071 | domain_loss:    0.00078 | bdc_loss:    0.00003\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 18200.00000 | total_loss:    0.00161 | domain_loss:    0.00179 | bdc_loss:    0.00003\n",
      "1/1 [==============================] - 0s 11ms/step\n",
      "epoch: 18400.00000 | total_loss:    0.00061 | domain_loss:    0.00067 | bdc_loss:    0.00003\n",
      "1/1 [==============================] - 0s 9ms/step\n",
      "epoch: 18600.00000 | total_loss:    0.00110 | domain_loss:    0.00122 | bdc_loss:    0.00002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 18800.00000 | total_loss:    0.00095 | domain_loss:    0.00105 | bdc_loss:    0.00002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 19000.00000 | total_loss:    0.00096 | domain_loss:    0.00106 | bdc_loss:    0.00002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 19200.00000 | total_loss:    0.00059 | domain_loss:    0.00065 | bdc_loss:    0.00002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 19400.00000 | total_loss:    0.00105 | domain_loss:    0.00116 | bdc_loss:    0.00002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 19600.00000 | total_loss:    0.00135 | domain_loss:    0.00149 | bdc_loss:    0.00002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "epoch: 19800.00000 | total_loss:    0.00108 | domain_loss:    0.00120 | bdc_loss:    0.00002\n",
      "1/1 [==============================] - 0s 10ms/step\n",
      "Training time cost:   44.01981 seconds\n"
     ]
    }
   ],
   "source": [
    "epoch_number_list = []\n",
    "total_loss_list = []\n",
    "domain_loss_list = []\n",
    "boundary_loss_list = []\n",
    "ux_list = []\n",
    "dudx_list = []\n",
    "d2udx2_list = []\n",
    "pde_model_train.load_weights('picn_initial_weights.h5')\n",
    "pde_model_train.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss=\"mse\", loss_weights = [0.9, 0.1])\n",
    "\n",
    "# Record the training time cost\n",
    "start = time.time()\n",
    "\n",
    "# Training\n",
    "pde_model_train.fit(x=training_input_data, \n",
    "                    y=training_label_data, \n",
    "                    epochs=20000, verbose=0,\n",
    "                    callbacks=callbacks)\n",
    "\n",
    "# Record the training time cost\n",
    "end = time.time()\n",
    "print(\"Training time cost: {:10.5f} seconds\".format(end - start))\n",
    "time_costs_text_file = open(\"time_costs.txt\", \"w\")\n",
    "time_costs_text_file.write(\"Training time cost: {:10.5f} seconds\".format(end - start))\n",
    "time_costs_text_file.close()\n",
    "\n",
    "# Load the best weights\n",
    "pde_model_train.load_weights('picn_best_weights.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. LOSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=pd.DataFrame({'epoch': epoch_number_list, \n",
    "                 'total_loss': total_loss_list, \n",
    "                 'governing_loss': domain_loss_list, \n",
    "                 'boundary_loss': boundary_loss_list})\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot( 'epoch', 'total_loss', data=df, marker='o', markerfacecolor='red', markersize=2, color='skyblue', linewidth=3)\n",
    "plt.plot( 'epoch', 'governing_loss', data=df, marker='', color='green', linewidth=2, linestyle='dashed')\n",
    "plt.plot( 'epoch', 'boundary_loss', data=df, marker='', color='blue', linewidth=2, linestyle='dashed')\n",
    "plt.legend()\n",
    "#plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 12ms/step\n"
     ]
    }
   ],
   "source": [
    "[_, u_pred, dudx_pred, d2udx2_pred] = pde_model.predict(training_input_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Results overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(x_domain,u_pred[0,:,0],label='u_pred')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('u_pred')\n",
    "plt.title('u-prediction')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(x_domain,dudx_pred[0,:,0],label='dudx_pred')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('dudx_pred')\n",
    "plt.title('dudx-prediction')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(12, 4))\n",
    "plt.plot(x_domain,d2udx2_pred[0,:,0],label='d2udx2_pred')\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('d2udx2_pred')\n",
    "plt.title('d2udx2-prediction')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Save the Process Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "picn_process_data = {'x_domain':x_domain,\n",
    "                     'epoch_number_list':np.asarray(epoch_number_list),\n",
    "                     'total_loss_list':np.asarray(total_loss_list),\n",
    "                     'domain_loss_list':np.asarray(domain_loss_list),\n",
    "                     'boundary_loss_list':np.asarray(boundary_loss_list),\n",
    "                     'ux_list':np.vstack(ux_list),\n",
    "                     'dudx_list':np.vstack(dudx_list),\n",
    "                     'd2udx2_list':np.vstack(d2udx2_list)}\n",
    "savemat('picn_process_data.mat', picn_process_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
